(595g) Optimization of Agile Energy Network for System Design and Dispatch Under Emergency

The development of renewable technology has provided more potential sources to the energy network while it has added the difficulty in the mathematical modeling of design and dispatch of multiple energy types in a wide regional area. The optimal construction of energy network should consider the satisfactory coverage of variable demand, the advantage of local energy resources, the reasonable distribution of energy facility, the normal operation cost and the consequence of short-time energy shortage under extreme conditions.  In local energy shortage, agile energy dispatching through an effective energy transportation network, targeting the minimum energy recovery time, should be a top priority, while the economic factor should be taken as the objective in routine energy utilization.

In this paper, a novel methodology is developed for energy network optimization for the capability of feasible energy dispatch under emergency of local energy shortage and the objective of minimal operation cost for routine operation under the constraint of demand requirement. This has included five stages of work.  First, potential energy network needs to be characterized, where the capacity, quantity, and availability of various energy sources are put into the quantification.  Second, the energy initial situation under emergency conditions needs to be identified.  Next step, the energy dispatch requirement is represented as constraint based on a developed MILP (mixed-integer linear programming) model in the third stage. Then, mathematical modeling with the objective function of minimal operation cost is constructed.  Finally, the sensitivity of the minimum dispatch time with respect to uncertainty parameters of energy loss and the construction cost with respect to energy dispatch capability is characterized by partitioning the entire space of parameters into multiple subspaces.  The efficacy of the developed methodology is demonstrated via a case study with in-depth discussions.